Clustering algorithms aim at finding dense regions of data based on similarities and
dissimilarities of data points. Noise and outliers contribute to the computational procedure of …

Big data storage and processing are among the most important challenges now. Among
data mining algorithms, DBSCAN is a common clustering method. One of the most important …

At present, the DBSCAN clustering algorithm has been commonly used principally due to its
ability in discovering clusters with arbitrary shapes. When the cluster number K is …

Clustering is an attractive technique used in many fields in order to deal with large scale
data. Many clustering algorithms have been proposed so far. The most popular algorithms …

Power demand prediction for buildings at a large scale is required for power grid operation.
The bottom-up prediction method using physics-based models is popular, but has some …

To cope with the rapid development of the Internet of Things (IoT) and the increasing
demand for real-time services, mobile edge computing (MEC) has become a promising …

With the rapid development of electric vehicles (EVs) in recent years, it is important to
understand the varied EV users for EV sector business innovation. Therefore, identifying …

Water flooding is one of widely used technique to improve oil recovery from conventional
reservoirs, but its performance in low-permeability reservoirs is barely satisfactory. Besides …

Data clustering divides the datasets into different groups. Incremental Density-Based Spatial
Clustering of Applications with Noise (DBSCAN) is a famous density-based clustering …

High-dimensional data is interpreted with a considerable number of features, and new
problems are presented in groups. The so-called" high dimension" is initially created to …